Customer Relationship Management (CRM) relates to various models and technologies for managing a company's relationships with its existing, past, and future customers. Some related art CRM systems automate, organize, and synchronize marketing, sales, customer service, and technical support. Customer engagement can be a critical component of social CRM solutions, wherein customer service agents contact customers via social media, and resolve their issues by engaging in asynchronous conversations with the customers. Some CRM systems integrate social media sites like Twitter™, LinkedIn™, and Facebook™ to track and communicate with customers to share their opinions and experiences with a company, products and services. The social media is a platform that connects millions of users together, and allows them to exchange and share common interests, topics or events. These platforms were developed for private usage among users, and later emerged as a new communication and marketing vector for the companies. Social media has grown at a consistent rate, and has become very relevant in the context of brand information.
Related art CRM solutions/systems provide a mechanism for the customer care agents to engage in conversations with customers to address their issues via telephone calls. Two new media that were later handled by CRM teams include synchronous online chat and emails. The new communication vectors provided by social media, essentially Facebook and Twitter, then gained the attention of the CRM teams CRM solutions need to constantly monitor the conversations, mainly for two components of the conversation, in order to provide an effective engagement. First, the status of the issue (issue status tracking) that is relevant to the conversation relates to customer satisfaction. Second, the nature of the dialogues (dialogue act tracking) engaged in by the agents relate to the effectiveness of the customer care agents and the conversation.
The issue status tracking is significant because interaction between a customer and a customer care agent takes place over a long period of time, during which the agent needs to return to the issues multiple times and address the customer's concern. Since one agent is usually involved with thousands of users at the same time, the agent needs to return to only the open issues. The agent should not spend time checking the resolved issues. Given the large number of customers and their data, it is important that the process of figuring out which issues are open and which are closed is automated. The dialogue act problem is important because it determines the nature of the dialogue that agents engage in with the customer. For example, the dialogue can be a simple greeting message, an acknowledgement, or a more involved statement that addresses the issue in a technical way. This dialogue act component helps CRM solutions to determine the effectiveness of the customer care agents and of conversations.
Typically, machine learning methods, such as supervised classification techniques, are employed to analyze these conversations that can track and measure effectiveness of these conversations in terms of issue resolution (issue status) and the nature of the dialogues that customer care agents engage in (i.e., dialogue act). However, due to the very sparse and informal nature of these social media conversations, existing machine learning techniques are not very accurate in terms of detecting issues status and dialogue act.